Submersed macrophyte communities before and after an episodic ice jam in the St. Clair and Detroit rivers

1989 ◽  
Vol 67 (8) ◽  
pp. 2364-2370 ◽  
Author(s):  
S. J. Nichols ◽  
D. W. Schloesser ◽  
P. L. Hudson
Keyword(s):  
Ground Truth ◽  
Early Spring ◽  
Total Abundance ◽  
Ice Jam ◽  
Submersed Macrophyte ◽  
Macrophyte Communities ◽  
Before And After ◽  
Permanent Effect ◽  
Free Growth ◽  
Low Altitude

We conducted surveys in 1983 and 1984 of submersed macrophyte communities off six islands in the St. Clair and Detroit rivers using low altitude aerial photography and ground-truth collections. Sample collections in 1984 followed one of the coldest winters on record, during which ice up to 4 m thick developed in areas that were normally ice-free. Growth of many of the 20 taxa collected was delayed in the spring of 1984, as compared with the spring of 1983. By September 1984, however, total abundance of all taxa was equal to or greater than that in 1983. The location, size, and shape of plant beds in September 1984 were similar to those in 1983. We concluded that the unusual ice jam in early spring of 1984 had little, if any, permanent effect on submersed macrophytes in the St. Clair and Detroit rivers.

scholarly journals The Effect of Evidence Transfer on Latent Feature Relevance for Clustering

Informatics ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 17
Author(s):  
Athanasios Davvetas ◽  
Iraklis A. Klampanos ◽  
Spiros Skiadopoulos ◽  
Vangelis Karkaletsis
Keyword(s):  
Mutual Information ◽  
Ground Truth ◽  
Original Data ◽  
Information Theoretic ◽  
Information Bottleneck ◽  
Latent Space ◽  
Before And After ◽  
Feature Relevance ◽  
Latent Representations ◽  
Transfer Method

Evidence transfer for clustering is a deep learning method that manipulates the latent representations of an autoencoder according to external categorical evidence with the effect of improving a clustering outcome. Evidence transfer’s application on clustering is designed to be robust when introduced with a low quality of evidence, while increasing the effectiveness of the clustering accuracy during relevant corresponding evidence. We interpret the effects of evidence transfer on the latent representation of an autoencoder by comparing our method to the information bottleneck method. Information bottleneck is an optimisation problem of finding the best tradeoff between maximising the mutual information of data representations and a task outcome while at the same time being effective in compressing the original data source. We posit that the evidence transfer method has essentially the same objective regarding the latent representations produced by an autoencoder. We verify our hypothesis using information theoretic metrics from feature selection in order to perform an empirical analysis over the information that is carried through the bottleneck of the latent space. We use the relevance metric to compare the overall mutual information between the latent representations and the ground truth labels before and after their incremental manipulation, as well as, to study the effects of evidence transfer regarding the significance of each latent feature.

scholarly journals Brain Tissue Conductivity Measurements with MR-Electrical Properties Tomography: An In Vivo Study

2020 ◽  
Author(s):  
Stefano Mandija ◽  
Petar I. Petrov ◽  
Jord J. T. Vink ◽  
Sebastian F. W. Neggers ◽  
Cornelis A. T. van den Berg
Keyword(s):  
Electrical Properties ◽  
Healthy Subjects ◽  
Ex Vivo ◽  
Simulated Data ◽  
Ground Truth ◽  
In Vivo Study ◽  
Conductivity Measurements ◽  
Tissue Conductivity ◽  
Before And After

AbstractFirst in vivo brain conductivity reconstructions using Helmholtz MR-Electrical Properties Tomography (MR-EPT) have been published. However, a large variation in the reconstructed conductivity values is reported and these values differ from ex vivo conductivity measurements. Given this lack of agreement, we performed an in vivo study on eight healthy subjects to provide reference in vivo brain conductivity values. MR-EPT reconstructions were performed at 3 T for eight healthy subjects. Mean conductivity and standard deviation values in the white matter, gray matter and cerebrospinal fluid (σWM, σGM, and σCSF) were computed for each subject before and after erosion of regions at tissue boundaries, which are affected by typical MR-EPT reconstruction errors. The obtained values were compared to the reported ex vivo literature values. To benchmark the accuracy of in vivo conductivity reconstructions, the same pipeline was applied to simulated data, which allow knowledge of ground truth conductivity. Provided sufficient boundary erosion, the in vivo σWM and σGM values obtained in this study agree for the first time with literature values measured ex vivo. This could not be verified for the CSF due to its limited spatial extension. Conductivity reconstructions from simulated data verified conductivity reconstructions from in vivo data and demonstrated the importance of discarding voxels at tissue boundaries. The presented σWM and σGM values can therefore be used for comparison in future studies employing different MR-EPT techniques.

Effects of forestry activities (clearfelling) on stream macroinvertebrate fauna in south-western Australia

1994 ◽  
Vol 45 (6) ◽  
pp. 963 ◽  
Author(s):  
IO Growns ◽  
JA Davis
Keyword(s):  
Species Richness ◽  
Community Structure ◽  
Western Australia ◽  
Macroinvertebrate Community ◽  
Total Abundance ◽  
Inorganic Solids ◽  
South West ◽  
Before And After ◽  
Stream Macroinvertebrate ◽  
Macroinvertebrate Fauna

The effects of forestry activities on macroinvertebrate community structure were examined in the headwaters of Carey Brook in the south-west of Australia. The fauna at four sites on an upland stream that ran through a logging coupe were compared, before and after clearfelling, with the fauna at four nearby undisturbed sites. Mean species richness and mean total abundance declined at the treatment sites relative to the control sites after the commencement of clearfelling activities. The composition of the macroinvertebrate fauna in the disturbed stream changed in comparison with that in the undisturbed sites after logging started but returned to the pre-logging composition after winter and spring rains had stopped. The observed changes in the macroinvertebrate fauna occurred during the periods of high loads of suspended inorganic solids at the treatment sites. The possible reasons for the observed results are discussed.

The decline of Myriophyllum spicatum in a eutrophic Wisconsin lake

1980 ◽  
Vol 58 (5) ◽  
pp. 527-535 ◽  
Author(s):  
Stephen R. Carpenter
Keyword(s):  
Exotic Species ◽  
Myriophyllum Spicatum ◽  
Simple Wave ◽  
Wave Pattern ◽  
Single Factor ◽  
Submersed Macrophyte ◽  
Macrophyte Communities ◽  
Biomass Dynamics

The recent changes in the submersed macrophyte communities of Lake Wingra, Wisconsin, have been dominated by the dynamics of one exotic species. After a decade of abundance, Myriophyllum spicatum has undergone a sustained decline in the Madison lakes. A pattern of explosive growth followed by declining abundance may describe most M. spicatum invasions.No simple single-factor explanation can adequately account for the biomass dynamics of M. spicatum in Lake Wingra. The decline in M. spicatum biomass appears to be the result of a complex of synergistically interacting factors, perhaps including nutrients, epiphytes, competitors, and parasites or pathogens.Invasions of lake districts by M. spicatum are hypothesized to follow a wave pattern. When interlake distances are accounted for, departure from a simple wave is hypothesized to result from lags in infestation of lakes which are not susceptible to colonization by M. spicatum. Several testable hypotheses are suggested by this view of M. spicatum invasions.

scholarly journals Approaching Freshet beneath Landfast Ice in Kugmallit Bay on the Canadian Arctic Shelf: Evidence from Sensor and Ground Truth Data

ARCTIC ◽  
2009 ◽  
Vol 61 (1) ◽  
pp. 76 ◽  
Author(s):  
Tony R. Walker ◽  
Jon Grant ◽  
Peter Jarvis
Keyword(s):  
Arctic Ocean ◽  
Ground Truth ◽  
Open Water ◽  
Early Spring ◽  
The Arctic ◽  
Ground Truth Data ◽  
The North ◽  
Landfast Ice ◽  
North American Arctic ◽  
Mackenzie River

The Mackenzie River is the largest river in the North American Arctic. Its huge freshwater and sediment load impacts the Canadian Beaufort Shelf, transporting large quantities of sediment and associated organic carbon into the Arctic Ocean. The majority of this sediment transport occurs during the freshet peak flow season (May to June). Mackenzie River-Arctic Ocean coupling has been widely studied during open water seasons, but has rarely been investigated in shallow water under landfast ice in Kugmallit Bay with field-based surveys, except for those using remote sensing. We observed and measured sedimentation rates (51 g m-2 d-1) and the concentrations of chlorophyll a (mean 2.2 ?g L-1) and suspended particulate matter (8.5 mg L-1) and determined the sediment characteristics during early spring, before the breakup of landfast ice in Kugmallit Bay. We then compared these results with comparable data collected from the same site the previous summer. Comparison of organic quality in seston and trapped material demonstrated substantial seasonal differences. The subtle changes in biological and oceanographic variables beneath landfast ice that we measured using sensors and field sampling techniques suggest the onset of a spring melt occurring hundreds of kilometres farther south in the Mackenzie Basin.

scholarly journals An approach for detecting changes related to natural disasters using Synthetic Aperture Radar data

2015 ◽  
Vol XL-7/W3 ◽  
pp. 819-826 ◽  
Author(s):  
N. Milisavljevic ◽  
D. Closson ◽  
F. Holecz ◽  
F. Collivignarelli ◽  
P. Pasquali
Keyword(s):  
Synthetic Aperture Radar ◽  
Ground Truth ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Ground Truth Data ◽  
Synthetic Aperture Radar Data ◽  
Before And After ◽  
The Impact ◽  
Aperture Radar

Land-cover changes occur naturally in a progressive and gradual way, but they may happen rapidly and abruptly sometimes. Very high resolution remote sensed data acquired at different time intervals can help in analyzing the rate of changes and the causal factors. In this paper, we present an approach for detecting changes related to disasters such as an earthquake and for mapping of the impact zones. The approach is based on the pieces of information coming from SAR (Synthetic Aperture Radar) and on their combination. The case study is the 22 February 2011 Christchurch earthquake. <br><br> The identification of damaged or destroyed buildings using SAR data is a challenging task. The approach proposed here consists in finding amplitude changes as well as coherence changes before and after the earthquake and then combining these changes in order to obtain richer and more robust information on the origin of various types of changes possibly induced by an earthquake. This approach does not need any specific knowledge source about the terrain, but if such sources are present, they can be easily integrated in the method as more specific descriptions of the possible classes. <br><br> A special task in our approach is to develop a scheme that translates the obtained combinations of changes into ground information. Several algorithms are developed and validated using optical remote sensing images of the city two days after the earthquake, as well as our own ground-truth data. The obtained validation results show that the proposed approach is promising.

scholarly journals An example of SAR-derived image segmentation for landslides detection

2018 ◽  
Author(s):  
Giuseppe Esposito ◽  
Alessandro Cesare Mondini ◽  
Ivan Marchesini ◽  
Paola Reichenbach ◽  
Paola Salvati ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Rapid Assessment ◽  
Ground Truth ◽  
Radar Data ◽  
Mass Movements ◽  
Ground Truth Data ◽  
Before And After ◽  
Satellite Radar ◽  
Gis Software ◽  
Derived Data

A rapid assessment of the areal extent of landslide disasters is one of the main challenges facing by the scientific community. Satellite radar data represent a powerful tool for the rapid detection of landslides over large spatial scales, even in case of persistent cloud cover. To define landslide locations, radar data need to be firstly pre-processed and then elaborated for the extraction of the required information. Segmentation represents one of the most useful procedures for identifying land cover changes induced by landslides. In this study, we present an application of the i.segment module of GRASS GIS software for segmenting radar-derived data. As study area, we selected the Tagari River valley in Papua New Guinea, where massive landslides were triggered by a M7.5 earthquake on February 25, 2018. A comparison with ground truth data revealed a suitable performance of i.segment. Particular segmentation patterns, in fact, resulted in the areas affected by landslides with respect to the external ones, or to the same areas before the earthquake. These patterns highlighted a relevant contrast of radar backscattering values recorded before and after the landslides. With our procedure, we were able to define the extension of the mass movements that occurred in the study area, just three days after the M7.5 earthquake.

scholarly journals An example of SAR-derived image segmentation for landslides detection

2018 ◽  
Author(s):  
Giuseppe Esposito ◽  
Alessandro Cesare Mondini ◽  
Ivan Marchesini ◽  
Paola Reichenbach ◽  
Paola Salvati ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Rapid Assessment ◽  
Ground Truth ◽  
Radar Data ◽  
Mass Movements ◽  
Ground Truth Data ◽  
Before And After ◽  
Satellite Radar ◽  
Gis Software ◽  
Derived Data

A rapid assessment of the areal extent of landslide disasters is one of the main challenges facing by the scientific community. Satellite radar data represent a powerful tool for the rapid detection of landslides over large spatial scales, even in case of persistent cloud cover. To define landslide locations, radar data need to be firstly pre-processed and then elaborated for the extraction of the required information. Segmentation represents one of the most useful procedures for identifying land cover changes induced by landslides. In this study, we present an application of the i.segment module of GRASS GIS software for segmenting radar-derived data. As study area, we selected the Tagari River valley in Papua New Guinea, where massive landslides were triggered by a M7.5 earthquake on February 25, 2018. A comparison with ground truth data revealed a suitable performance of i.segment. Particular segmentation patterns, in fact, resulted in the areas affected by landslides with respect to the external ones, or to the same areas before the earthquake. These patterns highlighted a relevant contrast of radar backscattering values recorded before and after the landslides. With our procedure, we were able to define the extension of the mass movements that occurred in the study area, just three days after the M7.5 earthquake.

Classification of UAV and bird target in low-altitude airspace with surveillance radar data

2019 ◽  
Vol 123 (1260) ◽  
pp. 191-211 ◽  
Author(s):  
W. S. Chen ◽  
J. Liu ◽  
J. Li
Keyword(s):  
Ground Truth ◽  
Radar Data ◽  
Motion Models ◽  
Algorithm Comparison ◽  
Aerial Vehicle ◽  
Low Altitude ◽  
The Time Domain ◽  
Model Conversion ◽  
Traditional Surveillance

ABSTRACTIn order to ensure low-altitude safety, a tracking and recognition method of unmanned aerial vehicle (UAV) and bird targets based on traditional surveillance radar data is proposed. First, several motion models for UAV and flying bird targets are established. Second, the target trajectories are filtered and smoothed with multiple motion models. Third, by calculating the time-domain variance of the model occurrence probability, the model conversion probability of the target is estimated, and then the target type is identified and classified. The effectiveness and robustness of the algorithm is demonstrated by several groups of Monte Carlo simulation experiments, including setting different recognition steps, different model transformation probability, filtering and smoothing algorithm comparison. The algorithm is also successfully applied on the ground-truth radar data collected by the low-altitude surveillance radar at airport and coastal environments, where the targets of UAVs and flying birds could be tracked and recognised.

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